Journal of Surgical Sciences Vol.8, No.2, April – June 2021 ORIGINAL ARTICLE ATTEMPTING TO INCREASE THE CLINICAL DIAGNOSTIC RATE OF ACUTE INTESTINAL ISCHEMIA USING MACHINE LEARNING ALGORITHMS Pîrvu Cătălin Alexandru1,2, Cristian Nica1,2, Mărgăritescu Dragoș3,4, Pătrașcu Ștefan3,4, Valeriu Șurlin3,4, Konstantions Sapalidis5,6, Eugen Georgescu3,4, Ion Georgescu3,4, Stelian Pantea1,2 1Pius Brânzeu County Emergency Clinical Hospital Timisoara, Romania 2Victor Babeș Medicine and Pharmacy University Timisoara, Romania 3Clinical County Emegrency Hospital of Craiova, Romania 4University of Medicine and Pharmacy Craiova, Romania 53rd Department of Surgery, “AHEPA” University Hospital, Thessaloniki, Greece; 6Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece Corresponding author: Cristian Nica E-mail:
[email protected] Abstract Acute intestinal ischemia (AMI) is a life-threatening surgical emergency where more than half of the affected patients do not survive. In spite of the medical advance, mortal-ity rates remain high due to late diagnosis, when proper surgical management and reperfusion techniques do not conclude to a successful outcome. The current study aims to find a proper diagnosis method with a high-reliability rate using machine learning (ML) algorithms. Methods: In this prospective cross-sectional study, we have collected and evaluated over the course of two years a total of 147 patients with a clini-cal presentation resembling acute mesenteric ischemia. Five ML algorithms, including Random Forest, Logistic Regression, Gradient Boosted Trees, Naive Bayes, and Multi-ple Layer Perceptron, were compared for their reliability in diagnosing acute intestinal ischemia by using regular blood tests performed in the emergency room (ER), on top of the main clinical characteristics of the researched condition.